Network boosting for BCI applications

Authors:
Shijun Wang;Zhonglin Lin;Changshui Zhang
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China
Venue:
DS'05 Proceedings of the 8th international conference on Discovery Science
Year:
2005

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Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Network game and boosting

ECML'05 Proceedings of the 16th European conference on Machine Learning

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Abstract

Network Boosting is an ensemble learning method which combines learners together based on a network and can learn the target hypothesis asymptotically. We apply the approach to analyze data from the P300 speller paradigm. The result on the Data set II of BCI (Brain-computer interface) competition III shows that Network Boosting achieves higher classification accuracy than logistic regression, SVM, Bagging and AdaBoost.